Network Working Group                                           Y. Zha
Internet Draft                                     Huawei Technologies
Intended status: Informational
Expires: April 2017


                                                      October 31, 2016


                Deterministic Latency Network Framework
                       draft-zha-dln-framework-00


Abstract

   More and more real time applications, such as VR gaming, high
   frequency trading, Internet of vehicle, require accurate latency
   guarantee or bound, during service provisioning. Providing End-to-
   End latency guarantee across Internet is increasingly attractive
   while challenging. The main problem with latency guarantee on
   Internet is that packet scheduling is still best-effort, and
   congestion that further introduces latency uncertainty cannot be
   avoided. This document presents a way of describing latency
   information that mostly rely on the congestion management such as
   queue scheduling, and a framework to use such information to
   guarantee End-to-End latency.

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   This Internet-Draft will expire on April 30, 2017.

Copyright Notice

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Table of Contents

   1. Introduction .................................................2
   2. Conventions used in this document ............................3
   3. End-to-End Latency Guarantee Problem .........................3
      3.1. Current QoS framework ...................................4
      3.2. Need of Modeling of Latency .............................5
      3.3. Need of Measurement of Flow Latency .....................6
   4. Latency Information Modeling and Interface Framework .........7
      4.1. Latency Aware PHB .......................................7
      4.2. Latency Aware Interface .................................9
   5. Security Considerations ......................................9
   6. IANA Considerations ..........................................9
   7. Acknowledgments ..............................................9
   8. References ...................................................9
      8.1. Normative References ....................................9
      8.2. Informative References .................................10

1. Introduction

   Latency sensitive applications, such as VR gaming, high frequency
   trading, Internet of vehicle, have become more and more popular.
   These applications often require a guaranteed or bounded End-to-
   End packet transmission latency. For example, the End-to-End
   latency bounds of and VR gaming are 1 millisecond and 5
   millisecond corresponding. Hence, mechanism achieving End-to-End
   latency guarantee or bound for latency sensitive applications is
   highly desirable [I-D.liu-dln-use-cases].


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   The well-known End-to-End QoS (Quality of Service) model is
   DiffServ (Differentiated Service) [RFC2474] that defines PHB (Per
   Hop Behavior) to provide different, relative service levels (or
   priority) for different packets. However, one key factor for
   latency during packet transmission is congestion, and congestion
   management such as packet queue scheduling further introduces
   latency uncertainty. DiffServ which only cares about providing
   differentiated service based on packet priority is not sufficient
   for the application that requires guaranteed End-to-End latency.

   Therefore, mechanism is desirable that can model the packet queue
   scheduling latency, then give the latency guarantee or bound of
   certain packet flow in each network node. It is also useful to
   define interface to expose such latency information to the upper
   level of the network to facilitate End-to-End latency guaranteed
   or bounded service.

2. Conventions used in this document

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL
   NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL"
   in this document are to be interpreted as described in [RFC2119].
   In this document, these words will appear with that interpretation
   only when in ALL CAPS. Lower case uses of these words are not to
   be interpreted as carrying [RFC2119] significance.

3. End-to-End Latency Guarantee Problem

   End-to-End latency is one of the most important metrics of network
   service provisioning. Latency sensitive service has become the
   main driving force of deterministic low latency network, which
   requires End-to-End latency guarantee or bound. Unlike relative
   low latency transport, End-to-End guaranteed or bounded latency
   requires the knowledge of the worst latency performance across the
   network, and the capability of latency guarantee in each network
   node. Current packet network is mainly best-effort, so End-to-End
   latency guarantee or bound is still difficult to achieve.

   The key issue is that latency guarantee requires latency
   description, or the capability of the network device, in order to
   further provide latency guarantee for certain traffic flow. In
   this section, the challenge of End-to-End latency guarantee, as
   well as the need of latency description for the network device is
   presented.





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    3.1. Current QoS framework

   End-to-End QoS provisioning is the typical approach today to
   provide better performance including latency, for the latency
   sensitive services. DiffServ [RFC2474] is a well-known QoS
   mechanism to provide differentiated service quality to different
   classes of traffic flow. DiffServ model is shown in Figure 1.

                      +-------+
                      |       |------------+
                  +-->| Meter |            |
                  |   |       |-+          |
                  |   +-------+ |          |
                  |             V          V
          +------------+  +--------+  +---------+  +-----------+
          |            |  |        |  | Shaper/ |  |           |
Packets =>| Classifier |=>| Marker |=>| Dropper |=>| Scheduler |=>
          |            |  |        |  |         |  |           |
          +------------+  +--------+  +---------+  |-----------+
               Figure 1 - DiffServ Model

   For classifiers, DiffServ uses code-points to describe the service
   level and drop level of packets. The service level and drop level
   are essentially relative performance description (or priority) for
   different classes of traffic flows. One key factor for latency is
   congestion, and congestion management such as packet queue
   scheduling further introduces latency uncertainty. A deterministic
   latency performance (e.g. guarantee or bound) description is still
   missing while necessary for latency sensitive traffic flows.

   For traffic conditioning policies, PHBs of DiffServ are applied,
   including BE (Best Effort), EF (Expedited Forwarding), CS (Class
   Selector) and AF (Assured Forwarding). These PHBs are designed for
   only a few flow aggregates, which imply that they cannot provide
   differentiated services for a potentially large amount of latency
   sensitive traffic flows with different latency requirements.

   For service provisioning (or packet scheduling) policies, they are
   decoupled from traffic conditioning policies and haven't received
   much attention. For example, in the case where an EF packet
   arrives at a network device that contains other EF packets in the
   queue, the latency of scheduling the EF packet is impacted by the
   size of the queue. Moreover, the order of different latency aware


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   flows arriving the EF queue hasn't been considered. Hence the
   specific packet scheduling policy in specific network device is
   import to the latency performance of the packet.

   In summary, the current DiffServ model is not sufficient for the
   application that requires guaranteed End-to-End latency. The
   problems can be listed as below.

   a) Current QoS mechanism like DiffServ is not well defined to
      support deterministic latency performance. For example,
      relative service level or packet priority can not address the
      congestion factor for latency, as well as the congestion
      management such as packet scheduling that introduces latency
      uncertainty. Current PHBs can only support a few flow
      aggregates which are not sufficient for different latency
      requirements.
   b) There is no user-/device-specific latency performance
      specification, or no control plane mechanism to assign user-
      /device-specific latency requirement to the network devices
      along the path. As a result, network device has no idea how
      fast a packet must be forwarded, and cannot adopt a suitable
      mechanism (e.g. queue scheduling) to guarantee latency.
   c) There is no mechanism supporting the measurement of flow
      latency inside of a network device, especially given certain
      PHB type and code-points of the flow. Such measurement will
      make End-to-End latency more visible, and thus is crucial for
      End-to-End latency oriented OAM.
   d) Service provisioning (or packet scheduling) policies are not
      specified. Packet scheduling policy and queue status are also
      key factors of latency and its uncertainty. Therefore packet
      scheduling policy must be considered to provide deterministic
      latency service for time sensitive flows.
   In a nutshell, how to explain the QoS value or how to make sure
   the QoS value can be used to guarantee latency performance is not
   well defined yet. Some extension to the current QoS model (e.g.
   new PHB) could be useful to solve these problems.

    3.2. Need of Modeling of Latency

   As mentioned in problem section, QoS value or packet priority
   cannot guarantee deterministic low latency. In another word, the
   same QoS value or priority doesn't guarantee same latency
   performance. In network device, various forwarding mechanisms and


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   interfaces introduce different latency that may be linked to the
   same priority code. There is still lack of latency performance
   information that can be used to provide latency guarantee service.

   The principle of Diffserv is focus on providing, describing
   differentiated service of traffic flow but not deterministic
   latency. Instead, queuing and scheduling, as a main part of
   latency and latency uncertainty, is out of DiffServ's major
   concern.

   PHB provides standard way of modeling of device forwarding
   behavior as well as how to handle each traffic flow. However, PHB
   description does not include queuing or scheduling information. In
   reality, latency is dominated by congestion control scheme, which
   is mainly queuing and scheduling in the network device to take
   care of multiple traffic flows arriving at the same port
   simultaneously.

   Therefore, extension to the current QoS model (e.g. new PHB) is
   desirable as a standard way to describe the latency performance of
   network device. With such standard latency model, network device
   is enabled to better manage the forwarding mechanism (e.g. queue
   scheduling) for the packet, in order to guarantee End-to-End
   latency for the service.

    3.3. Need of Measurement of Flow Latency

   Flow latency measurement is also very crucial to make sure the
   latency bound is not violated and useful for End-to-End latency
   aware OAM mechanism. There is a need to support the measurement of
   flow latency inside of a network device, especially given certain
   PHB type and code-points of the flow.

   Existing technologies such as OWAMP [RFC4656] and TWAMP [RFC5357]
   is focused on providing one way and two way IP performance metrics.
   Latency is one of metrics that can be used for End-to-End
   deterministic latency provisioning. Use OWAMP/TWAMP protocols or
   extension on that to support measurement of flow latency
   performance is feasible.

   Overlay based End-to-End latency measurement is another approach
   commonly adopted by service providers like CDN vendor. Such
   approach can be further enhanced by latency measurement inside
   network device, for better service provisioning, e.g. traffic
   steering and path selection.




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4. Latency Information Modeling and Interface Framework

   To provide better End-to-End latency guarantee, an extension of
   QoS framework is proposed to provide more accurate latency
   information of network device. Mechanisms for flow latency
   measurement and latency information exchange are also proposed.
   This work may introduce new interfaces or extension on existing
   interfaces.

    4.1. Latency Aware PHB

   Latency aware PHB provides latency performance information on
   network device with more accurate description on latency bound of
   queuing and scheduling mechanisms. In addition to classical PHB
   which focus on classifier, marker and shaper/dropper, latency
   aware PHB includes queuing and scheduling as well. The latency
   factor is mainly introduced by queuing and scheduling algorithm
   that handles congestion of multiple traffic flows. Congestion
   latency can be up to hundreds of milliseconds regarding buffer
   size. Implementations of various queuing, scheduling algorithms,
   and QoS policies introduce latency uncertainty. Moreover,
   different links cause different latency, as 1GE and 10GE will
   certainly cause different latency.

   The queue scheduling information model is proposed to describe
   latency based service capability and capacity of network nodes. As
   the assigned deterministic latency bound must be guaranteed, the
   network needs to know what kinds of latency based services can be
   provided by a network node for a flow with specific traffic
   profile.

   For latency based service capability, by referring to DiffServ
   design, a differentiated service model called latency slicing
   model is defined as follows. A network node provides multiple
   classes of latency-bounded services, and if a flow is allocated to
   a service class, then all its shaped packets can be sent out from
   the network node with waiting time no more than the predefined
   latency class bound. Such a latency-bounded service class is
   called a latency slice. For example, a network node may have
   multiple latency slices with latency bounds of 50us, 100us, 250us,
   1ms, etc. Notice that actual max flow latency may vary due to
   acceptance of new flows or finish of existing flows, but it will
   never exceed the corresponding latency slice bound.



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   The maximum packet length cannot exceed a predefined value called
   MaxSDU, otherwise other flows latency requirement may be possibly
   violated.

   A latency aware flow with specific traffic profile can be accepted
   by a latency slice only if the latency of any existing latency
   aware flow does not violate the latency bound of its latency slice.
   For example, when the traffic profile is defined by the leaky
   token model (b, r), two parameters MaxBurst B and MaxRate R
   representing the maximum allowable burst size and average rate are
   introduced to formulate the latency slice service capacity. In
   this case, a new flow can be accepted as long as b<=B and r<=R.
   MaxBurst and MaxRate can be adjusted to accommodate more latency
   sensitive flows in a particular latency slice, and lead to service
   capacity reduction of other latency slice.

   By hiding queue scheduling implementation details, the latency
   slicing information model is shown in Figure 2.

+--------------+--------------+
| Name         | Elements     |
+--------------+--------------+
|NodeID        |              |
+--------------+--------------+
|SliceID       |              |
+--------------+--------------+
|LatencyBound  |              |
|--------------+--------------+
|MaxSDU        |              |
|--------------+--------------+
|MaxRate       |              |
|--------------+--------------+
|MaxBurst      |              |
|--------------+--------------+
             Figure 2 - Latency Slice Information Model

   More detail of this latency Slice information model is under
   discussion and will be available in the next version.






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    4.2. Latency Aware Interface

   The queuing and scheduling information is typically localized in
   the network device. In another word, the current hop has no
   knowledge of how the flow was scheduled on last hop. So it is hard
   to guarantee End-to-End latency if latency information only
   affects locally. Latency performance such as queuing and
   scheduling information in network device needs to be exposed for
   End-to-End latency guarantee service provisioning.

   Basically, more information needs to be exchanged than the current
   DiffServ code-points. Existing approach such as [RFC7297] defines
   CPP that contains information of connection attributes, latency,
   loss, and so on.

   With latency performance information defined on previous section,
   an interface or mechanism to exchange such information is proposed.
   A simple approach can be an interface from network device to
   controller, which collects all the latency performance information
   of each hop, and then make a decision how to serve the flow at
   each hop. In this case, the controller tells each hop how to serve
   the flow with queuing, scheduling information that can be
   understood by each hop.

   The detail of latency aware interface is under discussion and will
   be available in the next version.

5. Security Considerations

   TBD

6. IANA Considerations

   This document has no actions for IANA.

7. Acknowledgments

   This document has benefited from reviews, suggestions, comments
   and proposed text provided by the following members, listed in
   alphabetical order: Jinchun Xu and Hengjun Zhu.

8. References

    8.1. Normative References

   [RFC2119] S. Bradner, "Key words for use in RFCs to Indicate
             Requirement Levels", BCP 14, RFC 2119, March 1997.


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   [RFC3393] C. Demichelis, "IP Packet Delay Variation Metric for IP
             Performance Metrics (IPPM) ", RFC 3393, November 2002.

   [RFC2474] K. Nichols, "Definition of the Differentiated Services
             Field (DS Field) in the IPv4 and IPv6 Headers", RFC 2474,
             December 1998.

   [RFC4656] S. Shalunov, "A One-way Active Measurement Protocol
             (OWAMP)", RFC 4656, September 2006.

   [RFC5357] K. Hedayat, "A Two-Way Active Measurement Protocol
             (TWAMP)", RFC 5357, October, 2008.

   [RFC7279] M. Boucadair, "IP Connectivity Provisioning Profile
             (CPP)", RFC 7297, July, 2014.

    8.2. Informative References

   [I-D.finn-detnet-problem-statement]

   Finn, N. and P. Thubert, "Deterministic Networking Problem
   Statement", draft-ietf-detnet-problem-statement-01 (work in
   progress), September 2016.

   [I-D.finn-detnet-architecture]

   Finn, N., Thubert, P., and M. Teener, "Deterministic Networking
   Architecture", draft-ietf-detnet-architecture-00 (work in
   progress), September 2016.

   [I-D.liu-dln-use-cases]

   Liu, X., "Deterministic Latency Network Use Cases", draft-liu-dln-
   use-cases-00 (work in progress), October 2016.

Authors' Addresses

   Yiyong Zha
   Huawei Technologies
   Email: zhayiyong@huawei.com






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